Characterization of Novel Solute Carriers in Humans, Mice and Flies: Solute Carriers in a Broad and Narrow Perspective

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UNIVERSITATISACTA

Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy 287

Characterization of Novel Solute Carriers in Humans, Mice and Flies

Solute Carriers in a Broad and Narrow Perspective

MIKAELA CEDER

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Dissertation presented at Uppsala University to be publicly examined in A1:107a, Biomedicinskt centrum (BMC), Husargatan 3, Uppsala, Friday, 16 October 2020 at 09:15 for the degree of Doctor of Philosophy (Faculty of Pharmacy). The examination will be conducted in English. Faculty examiner: Universitetslektor Anita Öst (Linköpings universitet).

Abstract

Ceder, M. 2020. Characterization of Novel Solute Carriers in Humans, Mice and Flies.

Solute Carriers in a Broad and Narrow Perspective. Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy 287. 81 pp. Uppsala: Acta Universitatis Upsaliensis. ISBN 978-91-513-0980-4.

The solute carrier family is the largest family of membrane-bound transporters in humans, with 430 members divided into 65 subfamilies. They transport various substrates across lipid barriers and are vital for absorption, distribution, metabolism and excretion in all cell types in the body. Despite being involved in vital functions, and their effect on both physiology and pathophysiology, many transporters are not characterized. The aim of this thesis was to study newly identified putative solute carriers of which little is known. In Paper I, the relationship of solute carriers in humans and fruit flies was studied. The study revealed that 54 of the 65 subfamilies in humans have one or more orthologues in fruit flies, and a total of 381 orthologues were identified in fruit flies. In Paper II, a comprehensive study of the putative solute carriers and their response to different sugar concentrations were performed. Several, but not all, putative solute carriers were altered in cell cultures maintained in media containing low or no glucose, and the expression normalized upon refeeding with glucose. Similar results were observed in fruit flies subjected to complete starvation or diets with varying sugar concentrations. Last, in Paper III and IV, characterization of one putative solute carrier, UNC93A, was performed. The studies revealed that UNC93A was a conserved protein with an abundant expression in the body of mice but with a restricted expression in fruit flies. The protein was found to possibly be expressed at, or close to, the plasma membrane of cells and to co-localize with Twik-Acid sensitive potassium channels. UNC93A was found to be important for the renal function in fruit flies and to affect survival and membrane potentials in cells. The findings of this thesis establish a high conservation of several putative solute carriers and that they have a highly dynamic regulation during fluctuating energy and glucose availability. Further, while several clear biological aspects of UNC93A was identified, the exact function of transporter proteins is cumbersome to find and more research about these transporters is needed to fully understand their mechanistic role and their association and/or involvement in health and sickness.

Keywords: Solute carrier, SLC, Putative SLCs, Major facilitator superfamily, MFS, Drosophila melanogaster, Glucose metabolism, UNC93A, CG4928

Mikaela Ceder, Department of Pharmaceutical Biosciences, Box 591, Uppsala University, SE-75124 Uppsala, Sweden.

urn:nbn:se:uu:diva-416506 (http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-416506)

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To my husband, daughter, and son,

with you I can accomplish everything

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List of Papers

This thesis is based on the following papers, which are referred to in the text by their Roman numerals.

I Ceder M.M., Fredriksson R (2020). Bridging the gap: The hu- man and D. melanogaster repertoire of Solute Carriers. Submit- ted manuscript

II Ceder M.M.*, Lekholm E.*, Klaesson A., Tripathi R., Schweizer N., Weldai L., Patil S., Fredriksson R. (2020) Glucose availability alters gene and protein expression of several newly classified and putative Solute Carriers in mice cortex cell culture and D. melanogaster. Frontiers in Cell and Developmental Biol- ogy, 8(579)

III Ceder M.M., Lekholm E., Hellsten S.V., Perland E., Fredriksson R. (2017) The neuronal and peripheral expressed membrane- bound UNC93A respond to nutrient availability in mice. Front Mol Neurosci, 10:351

IV Ceder M.M., Aggarwal T.*, Hosseini K.*, Patil S., Perland E., Maturi V., Williams M.J., Fredriksson R. (2020) CG4928 is vital for renal function in fruit flies and membrane potential in cells:

Characterizing the putative Solute Carrier UNC93A. Submitted manuscript

*Equal contribution

Reprints were made with permission from the respective publishers.

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Additional papers

V Perland E., Lekholm E., Eriksson M.M., Bagchi S., Arapi V., Fredriksson R. (2016) The putative SLC transporters Mfsd5 and Mfsd11 are abundantly expressed in the mouse brain and have a potential role in energy metabolism. PloS one, 11(6):e0156912

VI Roshanbin S., Lindberg F.A., Lekholm E., Eriksson M.M., Perland E., Åhlund R., Raine A., Fredriksson R. (2016) Histo- logical characterization of orphan transporter MCT14 (SLC16A14) shows abundant expression in mouse CNS and kidney. BMC Neurosci, 17(1):43

VII Williams M.J., Perland E., Eriksson M.M., Carlsson J., Er- landsson D., Laan L., Majebali T., Potter E., Fredriksson R., Benedict C., Schiöth HB. (2016) Recurrent sleep fragmenta- tion induces insulin and neuroprotective mechanisms in mid- dle-aged flies. Front Aging Neurosci, 8:180.

VIII Perland E*., Hellsten S.V*., Lekholm E., Eriksson M.M., Arapi V., Fredriksson R. (2017) The novel membrane-bound proteins MFSD1 and MFSD3 are putative SLC transporters af- fected by altered nutrient intake. J Mol Neurosci, 61(2):199- 214

IX Lekholm E., Perland E., Eriksson M.M., Hellsten S.V., Lind- berg F.A., Rostami J., Fredriksson F. (2017) Putative membrane-bound transporters MFSD14A and MFSD14B are neu- ronal and affected by nutrient availability. Front Mol Neurosci, 10:11

X Hellsten S.V., Eriksson M.M., Lekholm E., Arapi V., Perland E., Fredriksson R. (2017) The gene expression of the neuronal protein, SLC38A9, changes in mouse brain after in vivo star- vation and high-fat diet. PLoS One, 12(2):e0172917

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XI Hellsten S.V., Hägglund M.G., Eriksson M.M., Fredriksson R. (2017) The neuronal and astrocytic protein SLC38A10 transports glutamine, glutamate and aspartate, suggesting a role in neurotransmission. FEBS Open Bio, 7(6):730-746

XII Hellsten S.V., Tripathi R., Ceder M.M., Fredriksson R. (2018) Nutritional stress induced by amino acid starvation results in changes for Slc38 transporters in immortalized hypothalamic neuronal cells and primary cortex cells. Front Mol Biosci, 5:45 XIII Aggarwal T., Patil S., Ceder M.M., Hayder M., Fredriksson R.

(2020) Knockdown of SLC38 transporter orthologue – CG13743 reveals a metabolic relevance in Drosophila. Front Physiol, 10(1592)

*Equal contribution

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Abbreviations

AAR Amino acid response

AARE Amino acid response element ADP Adenosine diphosphate

AMP Adenosine monophosphate

AMPK AMP-activated protein kinase

APC Amino acid-polyamine-organocation superfamily ATF4 Activating transcription factor 4

ATP Adenosine triphosphate BBB Blood-brain barrier

BLAST Basic local alignment search tool C/EBP CCAAT/enhancer-binding protein ChoRE Carbohydrate response element

ChREBP Carbohydrate-responsive element binding protein CPA/AT Cation:proton antiporter/anion transporter

DAMS Drosophila activity monitor system DMT Drug/Metabolite Transporter Superfamily

ER Endoplasmic reticulum

ENaC Epithelial sodium channel FDA Food and Drug administration flyPAD Fly proboscis and activity detector

Gal4 Galactose-responsive transcription factor GAL4 GCN2 General control nonderepressible 2

GFP Green fluorescent protein GPCR G-protein-coupled receptor

HGNC Human gene nomenclature committee

HMM Hidden markov model

ICC Immunocytochemistry

IHC Immunohistochemistry

IT Ion transporter superfamily KCNK Potassium channel subfamily K MFS Major facilitator superfamily

MFSD Major facilitator superfamily domain

MLX Max-like protein

MLXIP MLX interacting protein MLXIP MLX interacting protein-like

mRNA Messenger RNA

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MT Malpighian tubules

mTOR Mammalian target of rapamycin

mTORC12 Mammalian (mechanistic) target of rapamycin complex 1–2 NHE Sodium/proton exchanger

NKCC Sodium/potassium/chloride cotransporter NSRE12 Nutrient sensing response element 1–2 Pfam Protein family

PLA Proximity ligation assay

PMDA Pharmaceuticals and Medical Device Agency PSI-BLAST Position specific iterative BLAST

qRT-PCR Quantitative real-time polymerase chain reaction

RNA Ribonucleic acid

RNAi RNA interference

SLC Solute Carrier

SPNS Sphingolipid transporters SSM Solid supported membrane SV2 Synaptic vesicle glycoprotein 2 SVOP/SVOPL SV2 related proteins

TASK TWIK related acid sensitive potassium channel TCA Tricarboxylic acid cycle

TCDB Transporter classification database TF Transcription factor

TFBS Transcription factor binding site TLR Toll-like receptor

TMS Transmembrane segment

tRNA Transfer RNA

TSS Transcription start site UAS Upstream activator sequence Unc-93 Uncoordinated-93 protein

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Introduction

The cell keeps the fundamental molecules of life within its lipid barrier, which also protects the inner milieu from the surrounding. This is true not only for the cell itself, but also organelles that lies within the cellular compartment. For the inner milieu to stay connected with the outer environment the membranes contain membrane-bound proteins that acts as “gate-keepers” [1, 2]. Mem- brane-bound proteins can roughly be divided into peripheral and integral membrane proteins [3]. Peripheral membrane proteins attach to the membrane temporarily; either to an integral membrane protein or directly to the lipid barrier through different anchors [4]. These proteins play vital roles as regulators both of transporters and receptors but also in cellular processes such as prote- olysis and signaling [4, 5]. Integral membrane proteins, e.g. receptors, channels and transporters, are located within the membrane [6]. Approximately 30

% of the human proteome encodes membrane-bound proteins [7], and 2000 genes are estimated to be transporters or transporter-related proteins [8]. Due to their vital functions, many membrane transporters are linked to diseases affecting almost all organs in the body [8-10], and around half of all pharmaceutical drugs targets membrane-bound proteins [11].

Transporters

Transporters enables water soluble molecules across the lipid membranes [12]. They are important for nearly all aspects of physiology, both in homeostasis, establishing electrochemical gradients and membrane potentials [8], and can be divided into passive and active transporters. Passive transporters, also known as facilitators or uniporters, transport substrates along a concentration gradient and will do so until the gradient is eliminated [13]. Uniporters are usually ion channels or carrier proteins that open upon stimulation by voltage, pressure or ligands [14]. Some examples of uniporters are the ion channels for sodium, calcium and potassium that are involved in the transmission of nerve impulses along neurons [15]; along with facilitative sugar transporters [16, 17] and nucleoside transporters [18]. Active transporters, on the other

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hand, move substrates across a membrane against a gradient and they are usually associated with accumulation of molecules crucial for the cell. Active transporters can either be primary active or secondary active. The primary active transporters use adenosine triphosphate (ATP) as energy to move molecules against the electrochemical gradient [12]. Sodium, potassium, protons, magnesium and calcium are common molecules transported by primary active transporters of ATPase-type e.g. the Na⁺/K⁺-ATPase [19] that maintain membrane potentials. Other examples of primary active transporters are found in the mitochondrial electron transport chain, whose transport is driven by redox energy [20], and ATP-binding cassette (ABC) transporters that translocate substrates across membranes [21]. Secondary active transporters, also known as solute carriers (SLCs), rely on the existing electrochemical gradient to cou- ple transport of substrates against the concentration gradient. They can either act as symporters or antiporters [22-24], moving one molecule with the electrochemical gradient to transfer another molecule against a concentration gradient. Symporters move two substrates in the same direction, e.g. the glucose symporter, while antiporters move two molecules in opposite directions, e.g.

Na⁺/Ca²⁺-exchanger, Figure 1. The most common ion used for coupled transport in mammals is sodium, whose electrochemical gradient is then used to complete the transport, while for transporters in bacteria, and also in the mitochondria, hydrogen is the most common co-transported ion [25].

Figure 1. Transporters allow movement of molecules; some requires ATP (primary active transporters), while others do not (secondary active and passive transporters).

Primary active transporters are pumps and ABC-transporters. SLCs are secondary active transporters and act as antiporters, symporters and uniporters. Passive trans- ports are ion channels and aquaporins. Credit to Somersault 18:24 (www.somer- sault1824.com) for figure components, shared under creative common license.

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Solute Carriers

SLCs are transmembrane proteins composed of hydrophobic alpha helices connected by hydrophilic extra- and intracellular loops [12, 26]. They are located in the plasma membrane [17, 27-29] and the membranes of mitochondria [30], endoplasmic reticulum (ER) [31], Golgi [32], proteolytic granule (e.g. lysosomes) [33] and vesicles [34-36].

SLCs were originally defined as ATP-independent transporters and protein sequences were sorted into the SLC superfamily based on their function rather than their structure and sequence identity. The SLC superfamily is the second largest family of membrane-bound proteins after GPCRs but are the largest family of transporters in humans [7, 37]. Current practice assigns a new protein sequence to a SLC family if it shares at least 20 % amino acid sequence identity to one other member of the family. This has resulted in a great degree of variation regarding structure and primary sequence compared to other large membrane-bound protein families e.g. GPCRs and voltage-gated ion channels [38, 39]. Today the SLC superfamily comprises 439 members divided into 65 subfamilies, SLC1–SLC65, listed in the SLC table (slc.bioparadigms.org).

Approximately two thirds of the SLCs are considered characterized and information regarding their expression, function and/or structure is available.

SLCs have diverse functions and they transport numerous compounds, both charged and uncharged, across membranes. They are capable of transporting larger molecules e.g. peptides, amino acids, sugars, neurotransmitters and therapeutic drugs, but also single ions. The transport is dependent on the concentration and electrochemical gradients, which make the transport highly connected to the concentration of substrates available in the extra- and intracellular compartments [12]. For example, there are transporters that move the same substrate in different tissues with different affinity, e.g. the SLC2 family (Glucose transporters, GLUTs) [16]; and families with a diverse range of substrates but all members are expressed in the same organelle, e.g. SLC25 family of mitochondrial transporters [30]. Not all SLCs transport solutes on their own, some are required as supporting subunits e.g. the SLC3 family [40]. In- terestingly, new functions have recently been assigned to the SLC proteins and a few members are now suggested to both function as transporters and as sensors/regulators for pathways involved in nutrient availability and transport e.g. SLC38A9 [41-43].

The functional diversity among the SLCs is a possibility to why there are multiple classification systems in use; i) the Human Gene Nomenclature Commit- tee (HGNC), ii) the Transport Classification Database (TCDB) and iii) the

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Protein Family Database (Pfam). The human SLCs are annotated and named according to a root system set by HGNC [44, 45]; “the SLC nomenclature”.

TCDB uses phylogenetic and functional data to categorize all recognized transporters into superfamilies [46, 47], while Pfam aims to cluster all transporters into protein families (clans) based on sequence similarities [48].

Therefore, SLCs populate several Pfam clans, where seven, so far, contain more than one SLC family; (I) Major Facilitator Superfamily (MFS) clan (CL0015), (II) Amino acid/Polyamine/organocation (APC) clan (CL0062), (III) Cation:Proton antiporter/Anion Transporter (CPA/AT) clan (CL0064)) (IV) Drug/Metabolite Transporter Superfamily (DMT) clan (CL0184), (V) Ion Transporter Superfamily (IT) clan (CL0182), (VI) MtN3-like clan of vesicle-trafficking cargo receptors (CL0141) and (VII) Mvin, MATE-like Super- family clan (CL0222).

The evolution of Solute Carriers

The physiological importance of transporters is evident as SLCs and SLC- related proteins are found in archaea, bacteria, plants and eukaryotes [37].

Several SLCs families are considered evolutionary old and have identified homologues in bacteria [37, 49, 50], e.g. SLC2, SLC22 and SLC25, and ancient members of algae [51], e.g. the SLC32, SLC36 and SLC38. Meanwhile, other families are more recent and are mainly identified in Animalia [37], e.g.

SLC5, SLC6, SLC8 and SLC18. Some of these families are important for the developed nervous system, suggesting that these families have evolved paral- lel to the development of a more complex central nervous system.

Protein sequences that presumably belong to the SLC superfamily are still being identified and several protein sequences have not yet been assigned to a SLC superfamily and are instead named putative (“atypical”) SLCs [52, 53].

Putative Solute Carriers

Currently 20 out of the 65 SLC families are categorized into the MFS clan (see slc.bioparadigms.org). In the early 1990s, members of the MFS clan were believed to be facilitated sugar transporters [49, 50, 54]. Today, it is known that these transporters have a diverse substrate profile and perform their transport as uniporters and cotransporters [54]. The MFS is the largest group of transporter proteins known, with over 10,000 sequenced members [55],

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present in both ancient life forms and mammals [56]. The amino acid sequence identity among SLCs and putative SLCs belonging to the MFS clan is low compared to other families, but their protein folding structure is conserved [57, 58]. The common structure for MFS proteins are an even number of alpha helices arranged in monomer or polymers, Figure 2, creating a globular pro- tein with 12–14 transmembrane spanning helices [57, 58].

Figure 2. Members of the MFS usually have an even number of transmembrane heli- ces arranged in monomers or polymers. Illustration exemplifies the general second- ary structure of MFS transporter with 12 alpha helices spanning the membrane.

Several putative SLCs belonging to the MFS clan were identified during genome annotation [59]. Most of them are classified into the MFS domain (MFSD#) nomenclature, however also previously named genes such as SV2, UNC-93 and SPNS belong among these putative SLCs. Recently, MFSD2A and MFSD2B (SLC59); MFSD3 (SLC33); MFSD4A and MFSD4B (SLC60);

MFSD5 (SLC61); MFSD10 (SLC22); SV2A, SV2B, SV2C, SVOP and SVOPL (SLC22); and SPNS1, SPNS2 and SPNS3 (SLC63) were sorted into SLC families (slc.bioparadigms.org) and more of these putative SLCs will probably be sorted into the SLC superfamily in the near future. Information regarding putative SLCs has increased over the past years and now the gene and protein expression in mouse are established for many of them [60-65].

Solute Carriers’ role in metabolism

The process of life is dependent on access to sufficient levels of essential solutes, e.g. amino acids, fatty acids, ions, nucleotides and sugars to maintain fundamental function within each cell, and to maintain homeostasis of important molecules. Mechanisms to sense and react to fluctuations in solutes have evolved to keep circulating nutrients within a narrow range both in unicellular and multicellular organisms [66-68]. SLCs make up a highly dynamic

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interface to supply the cell with major components needed for acidification, glycolysis, TCA cycle, neurotransmission and nucleic acid synthesis [66], Figure 3.

Figure 3. SLCs are important for several aspects of the cellular metabolism and main- tain influx and efflux of substrates that contribute to acidification, protein and nucleic acid synthesis, glycosylation, glycolysis and TCA cycle. Some examples of SLCs im- portant for these processes are: glucose transport (SLC2 and SLC5), amino acid transport (SLC1, SLC7:SLC3 and SLC38); mitochondrial transporters (SLC25;, mon- ocarboxylate transporters (SLC16); fatty acid transporters (SLC27); ion transporters (SLC9 and SLC30); nucleoside-sugar transporters (SLC35) and nucleobase trans- porters (SLC29). Credit to Somersault 18:24 (www.somersault1824.com) for figure components, shared under creative common license.

Amino acids are important for protein synthesis and catabolism during feeding and starvation, and they are essential as neurotransmitters and for food intake [68]. The levels of amino acids are important to keep constant and the levels are regulated through amino acid response (AAR), general control nonderepressible 2 (GCN2) and the mechanistic target of rapamycin (mTOR) pathways [68]. The AAR pathway is activated by low levels of essential amino acids and lead to an upregulation of the activating transcription factor 4

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(ATF4), which binds genes with amino acid response elements (AARE) and nutrient sensing response elements (NSRE-1 and 2), resulting in a reduced protein synthesis [69-71]. The GNC2 pathway is triggered by the accumulation of uncharged transfer RNAs (tRNAs) (normally covalent linked to a certain amino acid) that occurs during decreased levels of free amino acids. The activation of GNC2 leads to inhibitory phosphorylation of the essential activator of translation initiation, eukaryotic translation initiator factor 2α (eIF2α), also resulting in reduced protein synthesis [72]. The mTOR pathway, with its two distinct protein complexes, mTOR complex 1 (mTORC1) and mTOR complex 2 (mTORC2), integrates both extra- and intracellular signals to regulate anabolic and catabolic processes to coordinate nutrient levels [73, 74].

Each complex, mTORC1 and mTORC2, phosphorylates various effectors as a reaction to the varying energy demands of the cell. The mTORC1 is involved in promoting anabolic processes and limiting catabolic processes as a response to e.g. availability of nutrients such as amino acids and glucose, while mTORC2 promotes proliferation and survival as a response to growth factors [73, 75]. Leucine is an essential amino acid to initiate protein synthesis and cell growth through mTORC1 [76, 77]; as such, several SLC family members, e.g. SLC1A5, SLC3A2 and SLC7A5, are implemented in regulation of this complex since they maintain sufficient leucine levels upstream of this particular pathway. The mTORC1 is not an amino acid sensor itself, but recently the lysosomal arginine transporter, SLC38A9, was found to act as a sensor for mTORC1 by conveying the arginine and leucine availability directly to the complex [41-43].

Sugar, especially glucose, is a main source of energy for the body; and, there are several SLCs families that are specialized to help the cell to maintain glucose intake, storage, mobilization and breakdown. Glucose is found, be- sides in glycolysis and gluconeogenesis, in several fundamental processes in the cell including the pentose phosphate pathway, where it generates NADPH and ribose-5-phosphate; the hexosamine pathway; protein glycosylation; ser- ine biosynthesis as well as purine and glutathione biosynthesis [68, 78, 79].

Therefore, one can assume that SLCs that transport sugars and sugar metabo- lites are implemented in different steps in all of these different pathways.

Glucokinase, a hexokinase, catalyze the initial steps of glycogen synthesis and glycolysis, and is an important glucose sensor. Compared to other hexo- kinases, which mainly acts as phosphorylation machines, glucokinase has a low affinity for glucose and is therefore only activated by high levels of glucose. During conditions with low glucose, glucokinase help the body maintain transport of glucose, through SLC families, to organs with high metabolic rate

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such as the brain, kidneys and muscles [68]. Another important glucose sensor is the bi-directional SLC2A2 (GLUT2), which sense extracellular glucose levels. During hyperglycemia, GLUT2 mediates influx for storage and energy production, while during hypoglycemia it mediates efflux of glucose to the circulation [80]. In addition, members of the SLC5 family, in particularly SLC5A1 and A2, are cotransporters for glucose and sodium that provide the cell with glucose needed during glycolysis [66, 81]. The AMP-activated protein kinase (AMPK) and mTORC1 complex are also important elements for glucose sensing [68]. AMPK is activated by increasing levels of AMP and ADP, which induce glucose and fatty acid uptake and oxidation to increase ATP production [68]. The mTORC1 is regulated by glucose availability through the activity of the Rag GTPases, however, the mechanisms behind this are less clear [68]. Similar to the AAR pathway, there are carbohydrate response elements-binding proteins (ChREBP) that binds to carbohydrate response elements (ChoRE) to alter gene regulatory pathways in a glucose-dependent manner [82-86]. These elements mainly regulate genes involved in glycolytic and lipogenic processes, but also in insulin dependent pathways.

However, compared to the AAR pathway and the AAREs, the mechanisms are less clear. It is suggested that Max-like protein X (MLX) and MLX interacting protein (MLXIP, MondoA) form a complex that is regulated by glucose. It translocate from the outer mitochondrial membrane to the nucleus to enhance transcription of glycolytic target genes and regulators for glucose fluxes [82, 84, 86]. Similar theories have been proposed for the MLX:ChREBP complex [82-84].

SLC9A3 and SLC16A1 are reported to be important components when it comes to controlling the acidification of the cell [66]. SLC9A3 acts as a sodium:proton exchanger that prevents acidification in the gut [87, 88] and SLC16A1 mediates outward transport of lactate and protons produced by glycolysis [66, 89]. The mitochondrial carrier SLC25A1 exchanges malate and citrate:proton over the inner mitochondrial membrane to maintain the TCA cycle, whereas another member of the same family, SLC25A8 is responsible for transferring protons from the inner to the outer mitochondrial membrane for energy metabolism [30, 66]. The SLC27 and SLC59 families provide transport of fatty acids [90, 91] that instead of glucose can be used to yield ATP for the cell. Fatty acids are also important components of the membranes of the cell. In addition to the macronutrients, there are several SLC families that provide the cell with other essential molecules e.g. SLC39 family [92]

transport zinc that is important for secretion of secretory granules; SLC29 family [18] carries nucleobases over the nucleus membrane that are needed

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for the formation of nucleic acids and the SLC35 family [93] transports UDP- GlcNAc and UDP-galactose, which are biochemical precursors for polysac- charides.

Solute Carriers are important therapeutic targets

The list of diseases that SLCs are involved in is long, which come as no sur- prise due to their diverse expression and function within the body. Except hav- ing key roles in important cellular processes to maintain health, they are also the cause of disorders spread throughout the body [66]. Several SLCs are co- expressed in the same tissue and it is speculated that when this healthy co- expression of SLCs is perturbed in the body, it becomes sick [66, 94]. The SLC superfamily is highly complex and several families are involved in a va- riety of acute and chronic diseases. For example malfunction in amino acid/neurotransmitter transporters (SLC1, SLC6, SLC17), sugar transporters (SLC2) and ion transporters (SLC9, SLC12) are all linked to a spectrum of brain disorders (Alzheimer’s disease, autism, depression, epilepsy, Parkin- son’s disease and schizophrenia), lung diseases (cystic fibrosis and sleep-dis- ordered breathing), liver diseases (neonatal diabetes and Rotor syndrome) and conditions linked to the kidney (hypertension, renal tubular acidosis, Hartnup disorder and gout) [66].

As therapeutic targets, the drug can act on the SLC itself or the SLCs can be used as mediators for drug distributions. Within the SLC superfamily, members of the SLC6 family are the most exploited drug targets for therapeutic treatment of depression [95]. Other drug targets include the SLC5, SLC12, and SLC18 families [95]. Two good examples of SLCs acting as mediators of drug transport are the SLC21 (also known as SLCO) and SLC35 families;

where SLCO1B1 mediates sufficient drug distribution of statins in the liver and SLC35F2 is crucial for the delivery of a cancer drug, YM155, into tumor cells [94].

Currently, a little more than 40 % of all SLCs have been linked to a disease.

Through the advancement in sequencing techniques the contribution of genetic variance of SLCs is clearer and the linkage to disorders are constantly increasing. However, compared to other membrane-bound proteins, e.g.

GPCRs, the number of compounds targeting the SLCs is far lower [94, 96, 97]. Even with numerous SLCs linked to diseases only 12 SLCs are known to be utilized as drug targets [66, 95] compared to GPCRs with 475 approved drugs [96]. Between the years 2015 and 2018 only five drugs targeting SLCs

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were approved by the Food and Drug Administration (FDA) and the Japanese Pharmaceuticals and Medical Devices Agency (PMDA), compared to the GPCRs that in the end of 2017 had approximately 321 drug candidates in clin- ical trials [95-98]. There are several possible reasons for the delay in drug development towards SLCs, where the major challenge has been the technical barrier to characterize SLCs [94, 99, 100]. For examples, cell-based assays suffer problems due to overexpression difficulties and compensatory mecha- nism; the lack of high-quality antibodies gives partial understanding of the subcellular localization; cumbersome transport assays fail in screening cotran- sport and gradient necessary to a complete functional characterization. In addition, structure analysis of the integral membrane proteins is notoriously difficult [100]. In recent years there have been an increase in the number of de- termined protein structure of SLCs and several transport mechanisms, e.g.

rocker-switch and gated-pore mechanisms [100] have been established, which most likely can help the pharmaceutical industry understand and design ap- propriate ligands. Hopefully, the increase in research regarding SLCs will contribute to the development of more direct treatments as well as synergistic treatments to increase the efficacy of other pharmaceuticals through better distribution through targeted SLCs.

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Methodology

Imagine a world where no animal or cell-based testing was used and instead drug candidates were tested on humans directly. The time required for a new compound to reach the market could probably be cut in half, but along the way unpredicted adverse effects, that could even be fatal, would most likely occur.

Clearly, this is both unethical and impractical. Therefore, models are used in research to provide a system, a simple reflection of a complex reality, to de- fine, simulate, visualize and quantify a question to understand e.g. physiological phenomena and processes. Modelling by e.g. programmed mathematical calculations in computers (in silico) and/or living material such as cell cultures (in vitro), vertebrates and invertebrates (in vivo) are all used in the pharma- ceutical industry to find the best candidates for new drugs and to understand biological mechanisms. No model is superior over another, instead the key behind choosing a model is to consider the purpose of the research; which model that is most suitable to answer the research question and/or hypotheses, and also to keep in mind what the limitations are with each model.

In silico models to understand the evolutionary

connection among species and predict protein structures

A majority of current classifications systems are based on phylogeny, the evolutionary relationship between organisms. The evolutionary relationship can be predicted by using mathematical models to calculate similarities among genomes and proteomes, which then can be presented in phylogenetic trees.

Hidden Markov Model

The Hidden Markov model (HMM) is a tool for homology searches to identify protein sequences that are evolutionary conserved within a set of protein sequences. The model is based on the Markov process, where the probability calculations of possible events are dependent on previous observations (states). In the HMM, the Markov process has unobservable (hidden) states,

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but the parameters are still known (observed) [101, 102]. For example, in Pa- per I, the observed part of the model is the alignment of the human protein sequences used to build the HMM. Meanwhile the unobservable part of the model is the proteins not yet identified in a given proteome, in this case the proteome of D. melanogaster. The model calculates the probability of amino acid shifts over time and calculates the likelihood of how similar the assem- bling of protein sequences is between species.

Protein alignment

Protein alignments are used to arrange sequences to identify regions of similarity, which can indicate conserved function, structure and/or provide information regarding evolutionary relationship.

Pairwise and multiple alignments

Sequences alignment such as protein sequences can be performed on one sequence against another (pairwise) to search for identical and similar regions within the sequence, either over the sequence as whole (global) or in specific parts of the sequence (local). It can also be applied to several sequences at once (multiple alignment), where some of the tools available today perform a so-called progressive alignment, e.g. MAFFT [103] (Paper I–II and IV) and t- coffee [104, 105] (Paper III–IV). A progressive alignment is based on a hier- archal technique where the multiple alignment is built by performing several pairwise alignments starting with the most similar pair of the sequences and moves on to the least similar sequences. These methods are usually not opti- mal for a global alignment, but rather focus on conserved motifs within parts of the sequences [106].

Phylogenetic trees

A multiple alignment can later be used to build phylogenetic trees, which aim to reconstruct an evolutionary relationship among species. The outline of the phylogenetic tree is based on gathered information from groups of species, e.g. protein sequences, and represent the most likely hypothesis on how the species have evolved from one common ancestor. The different parts of the phylogenetic tree provide us with important information. The root (the begin- ning of the tree) tells us about the most recent and common ancestor of the tree, while the branches (the horizontal lines) represent events, but not time directly, and a series of ancestors, resulting in the species at the end. The pat- tern of the connection between branches represent the hypothesis on how the

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species have evolved from the ancestors. Each branch point, also referred to as an internal node, indicate an event leading to the split of one group into two [107, 108].

The phylogenetic tree will tell us which groups e.g. protein sequences that are more re- lated to each other, Figure 4. However, even if we know that modern organisms have evolved from ancient life-forms, we cannot specify the exact paths of evolution. There- fore, a tree only displays information-based hypotheses of relatedness and not definitive facts. The more we uncover about a lineage the more accurate the tree becomes.

There are several different approaches to construct a tree, where maximum parsimony and maximum likelihood are traditional alternatives. A tree built with maximum parsimony represent the simplest explanation supported by the given evidence to minimize the homoplasy (evolutionary events). The sim- plicity of this method fails to consider factors that affect how sequences might have evolved over time result in poor grouping. Meanwhile, when using maximum likelihood a model of evolution will be incorporated, where the highest probability of explaining the data will be the base for the construction of the tree [109]. Compared to maximum parsimony, maximum likelihood considers substitution of substrates e.g. amino acids as an evolutionary event, hence explaining the phylogenetic relationship in a more correct way. Bayesian interference [110], used in all papers presented in this thesis, also applies a model of evolution. However, the Bayesian interference is more efficient to construct phylogenetic trees compared to the more traditional techniques. This model has advanced compared to the regular maximum likelihood and has become more complex and efficient at the same time as it quantifies and addresses the source of uncertainty, prior and posterior probability [111]. Both the traditional maximum likelihood and Bayesian interference require a great main memory capacity and are usually less suited to handle a larger set of sequences. However, Bayesian interference has been speculated to produces

“too good to be true” result in contrast to bootstrap, a computer-based technique for assessing the accuracy of a statistical estimation, used in maximum parsimony and maximum likelihood [112-114]. On the other hand, bootstrap used in phylogeny has been criticized to be too conservative when providing assessments of confidence levels of observed clades in a tree [115].

Figure 4. Phylogenetic trees can tell us about the relationship be- tween the gathered data to pro- vide insight in family assortment or evolution. Group A, B, C and D have a common ancestor; how- ever, group B, C and D are more related to each other compared to group A.

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Another popular software for phylogenetic analysis is RAxML (Random- ized Axelerated Maximum Likelihood), which also uses maximum likelihood and bootstrap calculation to generate phylogenetic trees of larger datasets using less main memory and time [116, 117]. This software was used in Paper I to construct trees for the larger Pfam clans: MFS and APC.

Modeling and structure prediction of proteins

The amino acid sequence and protein folding reveals a lot about its function.

There are several methods to model macromolecules and these are deposit in the archive for macromolecular crystal structures, the Protein Data Bank (PDB) [118]. Large integral membrane proteins such as the SLCs have been difficult to model and only a few solved crystal structures exist [119]. A number of bacterial homologues [120-123] and some human SLCs e.g. SLC2A1 [124], SLC2A3 [125] and SLC42A3 [126] have solved protein structures but the number of solved structures is increasing. These structures have provided insight into the general structure of SLCs, with α-helices spanning the membrane connected by loops. More solved structures have also resulted in the development of online model and prediction software that can be used to esti- mate the structure of orphan proteins. Two such applications are PROTTER [127], used in Paper I and IV, and Phyre2 [128], used in Paper III and IV.

PROTTER is a web-based application that predicts the secondary structure of proteins by gathering information about protein topology, annotated features such as post-transcriptional modifications, and experimental proteomic data from several repositories [129]. Phyre2 is also a web-based tool, but this suite uses a template-based modeling approach, meaning that the sequence of interest is aligned to a sequence of known structure, i.e. homology modeling, which due to the growing availability of data regarding evolutionary relationship and sequencing projects together with the improved computer capacity has led to great success for this method. A model prediction by Phyre2 requires first information about homologous sequences, from which it predicts a secondary structure and builds a HMM that is used in a fold library scan, which later is used to build a backbone, predict loops and side chains to reveal a tertiary protein structure. However, this model has its limitations. For in- stance, modeling will be impossible or unreliable if no homologous template is detected [128].

Only a small part of the deposited protein sequences in the Uni- ProtKB/TrEMBL database has their experimental structure solved [130]. De- spite the increased quality of in silico models challenges with the methods still

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remains, and usually only fractions of the user-supplied protein sequence are represented. The computational software assemble proteins complexes by combining the fractions with information from different templates and struc- tural domains, leaving the model with some uncertainty and inaccuracy [131].

Still, protein structure prediction and modelling are very useful and aid in the progression of the biological research field [130].

In silico predictions of transcription factor binding sites for transcription factors

The advancement in genomics, transcriptomic and epigenomics has provided great opportunities to understand regulation of cellular transcription. Instead of focus on gene expression patterns that are associated with particular conditions, the research has advanced to interpret signaling pathways that regulates gene expression.

Genes are enhanced or silenced by transcription factors (TFs), e.g. ATF4 [71, 132, 133] and MLX [82, 83], that reacts to cellular events such as fluctu- ations in nutrients, as described earlier, and binds to transcription factor binding sites (TFBS) in the promoter region of the gene and/or within the gene itself. These binding sites can be predicted by entering the gene sequence and its promoter region into computer-based tools such as the Eukaryotic promoter database (EPD) [134], used in Paper II–III.

The search motif tools available via EPD is used to scan promoter regions with position weight matrices (PWM) of TFs; modelling binding specificity of the TFs and core promoter elements to find putative binding sites. The method is used to report TFBS by scanning a sequence for the presence of a specific motif that have greater similarity to the PWM than to the background.

PWMs are partially based on position frequency matrices (PFMs), which dis- play the frequency of each nucleotide at each position, and position probability matrices (PPMs) [135]. These are created by e.g. literature curation and experimental determination and the constructed PWMs are listed in open databases such as JASPER [136, 137]. During the past years, the PWM scanning method has improved, but still a remaining issue with the method is that it reports false positive predictions [138], most likely because the TFBS sequences are too short and too variable [135]. It is therefore very common that every gene in the genomes will have one match to the PWM of almost all TFs.

Furthermore, the PWM prediction does not consider chromatin structure or epigenetic regulation of the gene, meaning that it cannot provide information

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if the TFBS is accessible to the transcriptional machinery [139]. However, the PWM scanning method is a good tool to use to generate a list of preliminary putative binding sites that can further be interpreted.

In vitro and in vivo models in research

In vitro refers to experimental procedures that are performed under controlled conditions outside a living organism, while in vivo refers to experimental pro- cedures where a living organism is used e.g. animal studies. In vitro models make an excellent tool to explore mechanisms on the cellular level, as well as refine ideas and theories that later will be applied in in vivo models. Both in vitro and in vivo work is important to gain an overall picture of factors relevant for alterations in metabolism, gene and protein expression and to establish a proteins function.

In vitro models and their advantages and disadvantages

Human cell lines are a good alternative for reducing the use of multicellular organisms such as mice. The cell culture condition can be strictly controlled, are cost effective, easy to use and provide unlimited supply of material, and of course, by-pass ethical concern. They also provide a pure population which minimize biological variations, gives constant samples and usually gives re- producible results. Immortalized cell lines are today used in several vital sci- entific areas such as vaccine production, drug metabolism and toxicity studies, gene function studies and synthesis of biological compounds such as antibodies and therapeutic proteins [140]. However, cell lines are often genetically manipulated, which could cause alterations in their function and how they react to stimuli compared to a normal cell. Also, microbial infections can go unnoticed for a longer time-period in cell lines, which could lead to altered behavior and gene expression. Hence, great care needs to be taken when using cell lines and validation is needed to support key findings. Without knowledge from unicellular life forms there would probably be more unsolved questions about the human body, its physiology and disease mechanisms unsolved today [141]. However, even if the unicellular system can be strictly controlled and provides good research reproducibility, a limitation with the system is the lack of complexity the in vivo systems have.

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In vivo models and their advantages and disadvantages

Model organisms are considered a precious resource in research to understand processes involved in human physiology and disease. Mice, for example, have a high genetic and physiological similarities to humans and are invaluable as a model to understand evolutionary conserved biological processes. However, despite the value of these models, it is important to remember the impact evolution has had on our and the model organism’s development and adaptation to survive in different environment [142]. There are also fundamental differences between the model organisms and humans, e.g. the number of chromosomes, differences in anatomy, organ function and physiology. In addition, the fruit fly, for example, lack or differs greatly in important physiological functions compared to humans, such as the adaptive immune system, cardio- vascular system and the blood-brain barrier (BBB). In these regards, both mice and flies can be questioned as reliable models to study human diseases since it is differences in the linking between genes along with different efficacy of pharmaceutical drugs between the species. However, several findings from these model organisms have pioneer the field of medicine and pharmacy pointing to their importance in research [142-145].

Disadvantages of using mice compared to fruit flies are increased costs, the time consumed to perform a study and the need of ethical permission, hence, there are less possibilities to perform certain studies in mice. However, in comparison to mice, the fruit flies have the disadvantages of increased spon- taneous mutations within stocks, gene redundancy and a single fly is usually not sufficient to generate material for several molecular assays. In addition, the genetic similarity and protein identity between human and mouse make it possible to study protein localization using antibody-related assays such as immunohistochemistry (IHC), immunocytochemistry (ICC), proximity ligation assay (PLA) and western blot (WB). Protein detection relies on antibodies: a primary antibody recognizing a specific antigen epitope in the sample that is visualized with a labeled secondary antibody that amplifies a fluorescent or chemiluminescent signal corresponding to the protein. In Paper II, WB was used to investigate total mTOR, to provide information about the general metabolic state of the cell, and ICC was used to investigate the protein expression and localization of six putative SLCs (MFSD1, MFSD4A, MFSD11, MFSD14A, MFSD14B and UNC93A). In Paper III and IV, WB was used to confirm antibody specificity and insert of a CG4928 expression vector, re- spectively. ICC was used to visualize UNC93A expression and PLA was used to study co-localization between CG4928/UNC93A and TWIK related acid- sensitive K⁺ (TASK) channels, but no immunostaining was performed in flies.

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There are antibodies available for several targets related to human disease genes that can be used in fruit flies, but to study the protein localization of an orphan gene can be challenging, since these antibodies usually need extensive verifications to demonstrate that the immunostaining is correct.

D. melanogaster as a model organism

D. melanogaster has been used as a model organism for over 100 years, and is still widely used in genetic, immune system, development, physiologic and behavior research. There are several features that has made this insect a pop-

ular model organism; i) easy and cheap mainte- nance, ii) short generation time, iii) high fecundity, iv) easy to morphologically separate by eye, v) studies do not require ethical per- mits and vi) its complete genome was sequences in 2000 [146]. The size of the D. melanogaster genome is about 5 % of the size of the human genome, with approximately 15,500 genes spread on four chromosomes: three autosomes and one sex chromosome.

Around 4 % of all genes are suggested to encode transporter and/or transporter- related protein [148].

The complete life cycle of the fruit fly is around three weeks and the devel- opment (egg to adult) is approximately 8.5 days at 25 ºC, Figure 5. However, the life cycle and developmental timeline is highly affected by temperature.

Briefly, the fertilized eggs (embryos) hatch after 12 to 15 h, and the first instar larvae emerge. The larvae stage is around four days, where molting occurs twice, approximately 24 h (second instar) and 48 h (third instar) after hatching, Figure 5: The life span of D. melanogaster is three

weeks at 25 ºC. The fly goes through six stages of life:

embryo, first, second and third instar larva, pupa and adult. The embryo stage is around one day before the first instar larva emerges. All three larva stages are four to five days prior pupation. Metamorphosis takes four days before the adult fly emerge. Credit to Andreas Prokop for images of adult flies [147].

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before pupation. After four days of metamorphosis, the adult fly emerges [149].

There are several genetic tools that are currently used to influence the ge- netic patterns of D. melanogaster; galactose-responsive sequence, transposon mutagenesis and CRISPR/CAS9 system [150]. The Gal4-UAS system is a tool to regulate gene expression in D. melanogaster. The system is dependent on two parts: the Gal4 gene, a transcription factor from yeast [151], and an Up- stream Activator Sequence (UAS), a cis-acting regulatory sequence (CGG- N11-CCG), that Gal4 binds to and promote gene transcription [152]. To obtain flies that express the combination of Gal4 and UAS parental flies containing one part each will be mated, rendering their offspring to contain both, Figure 6. In the fruit fly, Gal4 is usually positioned under the control of a promoter or a tissue specific enhancer, which enables studies in specific subsets of cells and tissues, e.g. specific neurons, ubiquitous in the whole fly or a single gene of interest. The UAS, on the other hand, are present next to the gene of interest and can be used to express reporter genes such as green fluorescent protein (GFP) or interfering RNA (RNAi).

Figure 6. The Gal4-UAS system is a tool used to regulate and study genes in D. mel- anogaster. It is composed of two parts, the Gal4 transcription factor and a regulatory sequence, UAS. Gal4 is positioned under the control of a promoter and the UAS is introduced near a gene. This technique can be used to drive the expression of a re- porter gene e.g. GFP or to perform gene silencing using RNAi.

When placing the UAS next to a RNAi sequence, a double-stranded RNA is synthesized upon activation of the UAS. The produced RNA is complemen- tary to the gene of interest and is recognized as exogenous genetic material that will be eliminated by activating the RNAi pathway [153]. However, this

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technique has disadvantages. Research has shown that overexpression of Gal4 can lead to non-specific side-effects, usually linked to the immune system and stress responses [154], which must be considered when using the system.

It has been reported that a percentage of the lines produce non-specific phenotypes, hence it has become important to verify the experimental model [155]. For example, so-called GD RNAi lines have a transgene insertion that is P-element based and the insertion site is random [156]. KK RNAi lines were constructed to increase the efficiency and specificity of genetic phenotypes via a targeted insertion of the transgene, a hairpin construct, to a single loca- tion. However, 80 % of the GD lines are estimated to produce potent and gene- specific silencing, while only 75 % of the KK lines are predicted to produce a gene-specific phenotype [157, 158].

The genetic power of being able to control the gene expression has also led to the possibility to study alleles that are fatal in homozygous condition. These fly lines are kept alive by genetic markers, also called balancer chromosomes, to prevent recombination and death, but also to make it possible to separate the genetic modified offspring by their appearance.

Figure 7. Genetic markers are common to use in D. melanogaster. They prevent re- combination and are used to keep stocks of lethal alleles heterozygous. They can also be used to easily separate flies that express the genotype of interest. Some common balancers are Cy¹, Sb¹, w¹ and y¹. Credit to Andreas Prokop for images of flies [147].

Balancer chromosomes have three important properties; i) to carry dominant markers, ii) to make homozygosity affect reproduction negatively and iii) to suppress recombination with their homologues. Some common balancers are Cy¹(Curly wings), Sb¹(short and thick bristles), w¹ (eyes that lack pigmenta- tions, white) and y¹ (body and wing pigmentation, yellow), Figure 7.

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D. melanogaster as a model for human diseases and pharmacology

Even if the size difference is substantial between humans and D. melano- gaster, we are more alike than we think. Biochemical pathways, as well as biological and physiological features are conserved between D. melanogaster and mammals [159] and around 60 % of the genes in human are conserved in D. melanogaster [160]. A large set (75 %) of all known human disease genes have a homologue in D. melanogaster [160], hence, this model organism is promising to study numerous disease and to perform preclinical research.

Many similarities exist between the human and fruit fly body, and several human organs have a counterpart in the embryo, larvae and adult body of D.

melanogaster. The hindgut and Malpighian tubules (MTs) of the fruit fly fulfil similar functions as the human kidneys, which has provided a good system to study genetic and molecular basic functions of the excretory system. Excretion of waste and homeostasis of ions, water and sugars is a common and important biological function in both species. The important parts of the human kidneys are the nephrons, while the renal system of D. melanogaster consists of two pairs of tubules [161]: MTs and nephrocytes that reside close to the esophagus and heart. MTs work similar to the tubular parts of the nephron, and are responsible for absorption, reabsorption and secretion [162, 163], while nephrocytes remove waste products from the hemolymph in similar manners as the glomerulus in the kidneys [164]. Several SLCs families that are essential for excretion (SLC5, SLC6, SLC12 and SLC21) are conserved in the excretory system of the fruit fly [165].

The growth and development of all multicellular organisms depends on nutrient availability, hence both humans and fruit flies have developed sensory systems to monitor nutrients; amino acid and sugar homeostasis pathways [165]. SLC7, SLC13 and SLC29 orthologues, with the same or similar func- tions, are present in D. melanogaster, where all orthologues are linked to nor- mal body and organ growth as well as survival. The anatomic divergence be- tween the human and D. melanogaster brain are apparent, which make it dif- ficult to recapitulate morphological features observed in neurological diseases such as Alzheimer’s disease. Furthermore, the BBB of D. melanogaster is much simpler compared to humans, and it is only formed by glial cells surrounding the nervous system. However, the BBB of fruit flies does share similar functions and basic principles as the human BBB and several SLC families in this complex structure are well-preserved between the two species. Further- more, pathways are conserved e.g. neurotransmitters and mechanisms for

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neurotransmission storage, release and recycling are conserved [165-167];

hence, some defined phenotypes of human neurological disease can be repro- duced and used to identify and understand underlying genetic and molecular pathways. In addition, a majority of the metabolic pathways in humans are conserved in D. melanogaster, and recently, studies have highlighted that same or similar phenotypes observed in humans are found in flies when these metabolic pathways are challenged [168, 169].

Sometimes a prominent phenotype; seizures, disrupted movement patterns, obesity, edema and severe effects of pharmaceutical drugs can be seen by the naked eye. Usually, however, this is not the case and therefore behavioral as- says have been used with great success in D. melanogaster. Several sophisti- cated techniques have been developed for example the Drosophila Activity Monitor System (DAMS) [170], the fly Proboscis and Activity Detector (fly- PAD) [171] and Ramsay Assay [172].

DAMS is a useful system when studying general activity, sleep patterns and circadian rhythm as well as starvation resistance and survival. DAMS au- tomatically record the movement of single flies every time they pass a laser- beam. This system has been used to study the consequences of disrupted sleep- wake patterns [173]. The findings have established the importance of sleep and has mapped specific genes involved in the sleep-wake behavior. It has also been used to study the activity of flies with altered biological pathways important for movement [174] and circadian rhythm [175].

To monitor changes of metabolic pathways flyPAD has been used with success. It measures the number of meals (sips), the sizes of meals and the duration and the system has contributed to our understanding of genes and their connection to feeding. In addition, there are assays developed to measure circulating and stored macronutrients; something that is usually altered during obesity and diabetes. Together these two methods can provide information about genes and their impact on specific parts of metabolism.

It is of great importance to maintain the ionic and osmotic homeostasis during changes in external conditions by modulating the renal epithelial ion and water transport. The MTs of D. melanogaster offer a good model to study molecular mechanisms of the renal ion transport by using Ramsay assay. The Ramsay assay is used to measure fluid secretion rate per minute and by using electrophysiology the ion fluxes in each droplet can be measured [172].

Taken together, these similarities and techniques prove D. melanogaster to be a promising model to study the fundamental molecular pathways causing diseases linked to transporters and to identify novel pharmaceutics and therapeutic strategies [165]. The research and development processes in the

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pharmaceutical industry are costly and time consuming. Hence, this model organism enables an easy and cheap first stage research platform to characterize orphan transporters, to identify transporter that mediates the cellular influx and efflux of new and current pharmaceutics and how the genetic variants in membrane transporters contribute to pharmacokinetics and pharmacodynam- ics.

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Aim

Despite the important impact SLCs have on fundamental processes of life, research lags far behind other protein families and many members are still orphan. There have been technical barriers limiting the research on these integral membrane proteins, however, the advancement of today’s technologies and tools create a promising future for further research. SLCs are evolutionary old and well conserved in several model organisms. During the past years several new SLCs have been identified and there is a need to update the repertoire of orthologues proteins and create new databases. Therefore, a detailed mining of the complete SLC family in D. melanogaster was performed in Paper I.

Through phylogenetic analysis, putative SLCs have been identified and recently a few of those were classified into existing or new SLCs families. Little is known about these putative transporters. Several of them have been found to be affected by changes in amino acid level and during complete starvation.

However, little has been reported about what impact varying glucose levels have on their expression and therefore in Paper II changes of several putative SLCs were studied as a response to glucose availability in cortex cell cultures from mice and D. melanogaster. Several assumed putative SLCS have not yet been classified to a SLC family since the knowledge about them are limited.

Characterization of one orphan transporter was conducted to reveal its expres- sion and function in Paper III and Paper IV.

The specific aims for each paper were:

Paper I: The aim was to investigate the evolutionary relationship of SLCs in human and D. melanogaster, and to identify protein-protein orthologues, thereby bridging the gap of the repertoire of SLCs in these two species.

Paper II: The first aim was to perform a focused study on the gene regulation of putative and not yet annotated SLCs in response to limited glucose levels in cortex cell cultures. The second aim was to investigate gene expression alterations of putative SLCs in D. melanogaster

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subjected to starvation and different sugar concentration, and to see if the genes were regulated in similar patterns between the two model systems used. The third aim was to distinguish if the protein expression of the putative SLCs changes, i.e. if the transporters localization shifts from one compartment to another, as a response to glucose deprivation and starvation.

Paper III: The first aim was to investigate the protein expression pro- file and cellular localization of UNC93A in brain tissue and in primary cell cultures from mice. The second aim was to investigate gene expres- sion of Unc93a in the central nervous system and peripheral organs of mouse and how the gene is affected by food restrictions in both an in vivo and in vitro model.

Paper IV: Here the aim was to begin to functionally characterize the orphan transporter UNC93A, by studying its orthologue, CG4928, in D. melanogaster, by knockdown techniques to discover the function of the orthologue.

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Summary of research papers

Paper I: Bridging the gap – the human and D.

melanogaster repertoire of Solute Carriers

Phylogenetic analysis and protein alignment can be used to determine the evolutionary relationship between various biological species based on similarities and differences in their genetic characteristics. Previously, 348 orthologous proteins in D. melanogaster were identified as members of the SLC superfam- ily ([176], flybase.org). However, the focus of that study and the combined list on flybase.org were not to study the fly proteome in detail and search for specific protein orthologues. Some of the SLC classifications are based on assumptions and function rather than phylogenetics. Here, HMM for each Pfam cluster of SLCs was built using HMMBUILD. Related proteins in D. mel- anogaster were identified by running the entire D. melanogaster promote against the human Pfam HMMs with HMMSEARCH [102, 177]. Meanwhile, global protein alignment was used to investigate if the identified orthologues meet the current criterion of 20 % amino acid sequence identity. We identified 381 orthologous proteins in D. melanogaster and delineated them to their re- spective SLC family, Figure 8.

We found that in general the SLC superfamily is well conserved between these two species, highlighting their importance in cellular processes, but that there are 11 families that lack orthologues in D. melanogaster. This suggest that these 11 families have evolved later, probably to encounter the increased demands of a more complex biological system.

Compared to other large protein families, e.g. GPCRs, the protein identity between the 65 SLCs families are relatively low, and we found that the total sequence identity of all human SLCs and their orthologues in flies was below 30 %. The finding reveals the diversity within the family, where sometime the only connection among family members is that they transport similar solutes in an energy independent manner. This reflects the difficulties in classifying the SLCs and why the current classification system was quite recently adopted, leading to a delay in characterization. Hopefully, the annotation of SLC protein sequences in D. melanogaster and assigning them to families can

Characterization of Novel Solute Carriers in Humans, Mice and Flies: Solute Carriers in a Broad and Narrow Perspective

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